• Journal of Applied Biological Chemistry
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• v.62 no.3
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• pp.287-292
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• 2019

Pseudomonas tolaasii causes brown blotch disease on the oyster mushroom (Pleurotus ostreatus). Various pathogenic strains of P. tolaasii were isolated and divided into three subtypes, $P1{\alpha}$, $P1{\beta}$, and $P1{\gamma}$. For phage therapy, bacteriophages against to these subtype strains were applied to mushroom cultivation and very successful to prevent from the disease. In this study, bacteriophages were isolated against the representative strains of subtype pathogens and their polyclonal antibodies were synthesized to investigate structural relationship among capsid proteins of phages. Phage preparations over $10^{10}pfu/mL$ were injected to rabbit thigh muscle and polyclonal antibodies were obtained after three times of boost injection. Titers of the antibodies obtained were over $2{\times}10^7Ab/mL$ for the phage ${\phi}6264$, $1{\times}10^6Ab/mL$ for the phage ${\phi}HK2$, and $1{\times}10^7Ab/mL$ for the phage ${\phi}HK19$ and phage ${\phi}HK23$. High specific activities were observed between antibodies and the corresponding bacteriophages. Some cross-reactivities between the antibodies and non-corresponding bacteriophages were also measured. Antibody $Ab{\phi}6264$ inactivated all phages of $P1{\alpha}$ subtype and only phage ${\phi}HK16$ among $P1{\beta}$ subtype phages. Antibody $Ab{\phi}HK23$ of $P1{\gamma}$ subtype neutralized all phages of $P1{\beta}$ subtype as well as the phage ${\phi}HK23$, showing the widest phage-inactivation range. When the structural-similarity studies of phages were investigated by using phage antibodies, closeness obtained by phylogenetic analysis of 16S rRNA genes of pathogenic strains were quite different from that of polyclonal antibody-specific structural similarity of phage capsid proteins. In conclusion, there is weak correlation between the host strain specificity of bacteriophage and its capsid structural similarity measured by phage antibodies.

• Journal of Nutrition and Health
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• v.52 no.1
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• pp.6-16
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• 2019

Purpose: Dyslipidemia is a major risk factor for cardiovascular disease. Pine needles (Pinus densiflora seib et Zucc) are a traditional medicine used to treat dyslipidemia in clinical settings. This study examined the potential effects of sulgidduk, a Korean traditional rice cake containing pine needle juice to protect against dyslipidemia induced by a high-fat/sugidduk diet in a rat model. Methods: Twenty one male Sprague-Dawley rats were divided randomly into three groups: normal control (NC), Sulgidduk diet (SD), Sulgidduk diet containing pine needle juice (PSD). The blood lipid levels, production of lipid peroxide in the plasma and liver, total cholesterol and triglyceride in the liver and feces, antioxidant enzyme activities in plasma and erythrocytes were measured to assess the effects of PSD on dyslipidemia. Results: A high-fat/Sulgidduk diet induced dyslipidemia, which was characterized by significantly altered lipid profiles in the plasma and liver. The food intake was similar in the three groups, but weight gain and food efficiency ratio (FER) were reduced significantly in the PSD group compared to those in the SD group. The level of total cholesterol, LDL-cholesterol and TBARS in the plasma showed tendencies to decrease in the PSD group compared to those in the SD group. The levels of high-fat/Sulgidduk diet-induced sterol regulatory element-binding protein 2 (SREBP2) gene expression were reduced significantly in the PSD group. The supplementation of PSD reduced the hepatic triglyceride and total cholesterol levels significantly, and enhanced the fecal excretion of triglyceride and hepatic antioxidant enzyme activities compared to the SD group. Conclusion: These results suggest that the addition of 0.4% pine needle juice to Sulgidduk may be an alternative snack to control dyslipidemia.

• Korean Journal of Plant Resources
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• v.35 no.5
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• pp.641-651
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• 2022

The biodiversity research of barley, a functional food, is proceeding to conserve germplasms and develop new cultivar of barley to improve its functional effects. In this study, with 25,104 barley germplasms in the National Agrobiodiversity Center, South Korea, the biodiversity index of species was much lower (1.17) than the origins (24.73) because of the presence of a biased species, Hordeum vulgare subsp. vulgare, but the species and origin of germplasms were significantly different with regard to genetic traits. In the clustering analysis based on genetic traits, we found that 97% barley germplasms could mostly be distributed between 1~7 clusters out of a total of 15 clusters; 'normal and uzu type', 'lodging', and 'loose smut' were commonly represented in the 1~7 clusters and some clusters showed specific differences in five genetic traits including 'growth habit'. In correlation of each genetic trait, the infection of 'barley yellow mosaic virus' was highly correlated to 'number of grains per spike'. '1000 grain weight' was weakly correlated with seven genetic traits including 'number of grains per spike'. Our analysis for barley's biodiversity can provide a useful guide to the species' phenotypes that need to be collected to conserve biodiversity and to breed new barley varieties.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.